Health-science researchers often measure psychological constructs using multi-item scales
and encounter missing items on some participants. Multiple imputation (MI) has emerged as …

Missing data are a prevalent problem in psychological research that can reduce statistical
power and bias parameter estimates. These problems can be mostly resolved with multiple …

Adequately addressing missing data is a pervasive issue in the social sciences. Failure to
correctly address missing data can lead to biased or inefficient estimation of parameters …

The last 20 years has seen an uptick in research on missing data problems, and most
software applications now implement one or more sophisticated missing data handling …

The performance of multiple imputation in questionnaire data has been studied in various
simulation studies. However, in practice, questionnaire data are usually more complex than …

Missingness in intensive longitudinal data triggered by latent factors constitute one type of
nonignorable missingness that can generate simultaneous missingness across multiple …

Medical research often involves using multi‐item scales to assess individual characteristics,
disease severity, and other health‐related outcomes. It is common to observe missing data …

Multilevel data are often incomplete, for example, when participants refuse to answer some
items in a questionnaire or drop out of a study that involves multiple measurement …

Social scientists typically devote considerable effort to mitigating measurement error during
data collection but then ignore the issue during data analysis. Although many statistical …

Missing data are common in psychology research and can lead to bias and reduced power if
not properly handled. Multiple imputation is a state-of-the-art missing data method …